E-commerce, like classical commerce, adresses the problem of conciliating the different needs of customers, and the choices or even the purposes of the supply. A priori, by letting the customer explore freely the list of available products, e-commerce should enable him to maximize his satisfaction. Nevertheless, in 60 % of the cases the customer leaves without any purchase and the conversion rate visitor/customer does generally not exceed 15 %. In on-line sale contexts, one of the main limitative factor is the difficulty for the user to focus on products that satisfy his preferences, and in an orthogonal way, the difficulty for the supplier to guide potential customers. This difficulty increases with the size of the e-catalogue, which is typically large when the considered products can be configured: the search space is then combinatorial. The goal of our project is to study how configurators can help a client by guiding his choices, like recommendation systems do, without losing their ability to work on combinatorial domains. This would enable both to perform preference-based guided configurations or collaborative filtering in configuration contexts, and to build recommendation systems which propose the same interactivity than configurators. From a scientific point of view, the originality of this project relies on two main ideas: on the first hand, the use of learning techniques to solve combinatorial problems; on the other hand, the use of compilation approaches, not only for the catalogue or for customer indicators, but also for the recommendation model. This project is a fundamental research project which is planned for 3 years. It involves the Institut de Recherche en Informatique de Toulouse (IRIT), the Laboratoire en Informatique et Robotique et Microélectronique de Montpellier (LIRMM)' and the Centre de Recherches en Informatique de Lens (CRIL)', but is not purely academic. The participation of three industrial partners specialised in configuration softwares,, Cameleon software, IBL and Renault, provide the project with an expert point of view on actual needs, and a case study. The project deals with two important research domains for e-commerce, namely, the development of online recommender systems and configurators. It aims at developing the advising functionality and taking into account the customer's preferences in configuration-based systems, both with ``classical'' B2B configuration (technical object configuration) or with interactive exploration of a B2C catalogue (preference-based search or interactive exploration). To this end, this project proposes to pool the experience about those two kinds of system, studying jointly learning techniques, collaborative and compilation-based filtering and/or preference propagation. To the best of our knowledge, this approach is absolutely original with respect to the international state of the art techniques. This project is a revised version of the "BR4CP" projet submitted as a "ANR-Blanc" proposal for 2010 - it includes a new partner (IBM) that will (among others) reinforce the analysis and the validation tasks.

SMARTHEP is a consortium formed by academic and industrial partners on scientific, technological, and entrepreneurship aspects of real-time analysis. The focus of SMARTHEP is a central question in a data-rich environment: how to make the most of the available data to take decisions fast and efficiently, making the most of the available data. The main purpose of SMARTHEP is to train a new generation of inter-sector researchers and give them the tools to tackle this challenge, by processing large datasets in real- time, aided by Machine Learning and hybrid computing architectures. The results of SMARTHEP will benefit the HEP community in providing cutting edge technology and algorithms for the area of data selection (triggering) and particle detection, leading to precise measurement of the fundamental constituents of matter and enabling the discovery of new physics processes. The results of SMARTHEP include concrete commercial deliverables for industry in the fields of transport, finance and industrial decision-making processes. The training aspect of this work is crucial to SMARTHEP. The young investigators working within SMARTHEP will also be prepared as professionals in the upcoming and highly demanded area of Data Science. They will have the chance to gather work experiences and training in industry during their PhD degree and establish connections to companies, as well as acquire the skills that are necessary to a career in either industry or academia.